System and method for predicting cooling performance of arrangements of equipment in a data center

ABSTRACT

A system and method for evaluating equipment in an improper cluster in a data center, the equipment including a plurality of equipment racks, and at least one cooling provider. In one aspect, the method includes receiving data regarding each of the plurality of equipment racks and the at least one cooling provider, the data including a layout of the improper cluster of equipment racks and the at least one cooling provider, storing the received data, identifying at least one gap in the layout, determining cooling performance of at least one of the plurality of equipment racks based, at least in part, on characteristics of the at least one gap, and displaying the layout of the data center, wherein the layout includes an indication of the cooling performance of the at least one of the plurality of equipment racks.

BACKGROUND

1. Field of the Invention

At least one embodiment in accordance with the present invention relatesgenerally to systems and methods for data center management and design,and more specifically, to systems and methods for predicting coolingperformance of arrangements of equipment in a data center, includingimproper clusters of equipment in a data center.

2. Discussion of Related Art

In response to the increasing demands of information-based economies,information technology networks continue to proliferate across theglobe. One manifestation of this growth is the centralized network datacenter. A centralized network data center typically consists of variousinformation technology equipment, collocated in a structure thatprovides network connectivity, electrical power and cooling capacity.Often the equipment is housed of specialized enclosures termed “racks”which integrate these connectivity, power and cooling elements. In somedata center configurations, these rows are organized into hot and coldaisles to decrease the cost associated with cooling the informationtechnology equipment. These characteristics make data centers a costeffective way to deliver the computing power required by many softwareapplications.

Various processes and software applications, such as the InfrastruXure®Central product available from American Power Conversion Corporation ofWest Kingston, R.I., have been developed to aid data center personnel indesigning and maintaining efficient and effective data centersconfigurations. These tools often guide data center personnel throughactivities such as designing the data center structure, positioningequipment within the data center prior to installation and repositioningequipment after construction and installation are complete. Thus,conventional tool sets provide data center personnel with a standardizedand predictable design methodology.

SUMMARY OF THE INVENTION

A first aspect of the invention is directed to a computer-implementedmethod for evaluating equipment in an improper cluster in a data center,the equipment including a plurality of equipment racks, and at least onerack-based cooling provider. The method includes receiving dataregarding each of the plurality of equipment racks and the at least onecooling provider, the data including a layout of the improper cluster ofequipment racks and the at least one cooling provider, storing thereceived data, identifying at least one gap in the layout, determiningcooling performance of at least one of the plurality of equipment racksbased, at least in part, on characteristics of the at least one gap, anddisplaying the layout of the data center, wherein the layout includes anindication of the cooling performance of the at least one of theplurality of equipment racks.

In the method, determining cooling performance of the at least one ofthe plurality of equipment racks may include determining capture indexfor the at least one of the plurality of equipment racks, and the methodmay further include determining capture index for each of the pluralityof equipment racks based, at least in part, on characteristics of the atleast one gap. In the method, determining capture index may includedetermining airflow through the at least one gap, and determiningairflow may include determining airflow out of an aisle of the impropercluster through the at least one gap, and determining airflow into theaisle through the at least one gap. Further, determining coolingperformance of the at least one of the plurality of equipment racks mayinclude determining a value for capture index for the at least one ofthe plurality of equipment racks with each gap in the layout modeled asa blank panel. The method may further include determining a correctorvalue based on characteristics of the at least one gap, and applying thecorrector value to the value for capture index for the at least one ofthe plurality of equipment racks. The method may also includepositioning the equipment in the data center in accordance with thelayout.

Another aspect of the invention is directed to a system for evaluatingequipment in an improper cluster in a data center, the equipmentincluding a plurality of equipment racks, and at least one rack-basedcooling provider. The system includes an interface, and a controllerconfigured to receive data regarding each of the plurality of equipmentracks and the at least one cooling provider, the data including a layoutof the improper cluster of equipment racks and the at least one coolingprovider, store the received data in the system, identify at least onegap in the layout, and determine cooling performance of at least one ofthe plurality of equipment racks based, at least in part, oncharacteristics of the at least one gap.

The system may further include a display coupled to the controller, andthe controller may be further configured to display the layout of thedata center, wherein the layout includes an indication of the coolingperformance of the at least one of the plurality of equipment racks. Thecontroller may be further configured to determine capture index for theat least one of the plurality of equipment racks based, at least inpart, on characteristics of the at least one gap. In the system, thecontroller may be configured to determine airflow out of an aisle of theimproper cluster through the at least one gap, and determine airflowinto the aisle through the at least one gap. The controller may befurther configured to determine a value for capture index for the atleast one of the plurality of equipment racks with each gap in thelayout modeled as a blank panel. Further, the controller may beconfigured to determine a corrector value based on characteristics ofthe at least one gap, and apply the corrector value to the value forcapture index for the at least one of the plurality of equipment racks.

Another aspect of the invention is directed to a computer readablemedium having stored thereon sequences of instruction includinginstructions that will cause a processor to receive data regarding alayout of an improper cluster of equipment racks and at least onecooling provider, store the received data, identify at least one gap inthe layout, and determine cooling performance of at least one of theplurality of equipment racks based, at least in part, on characteristicsof the at least one gap.

The sequences of instruction may further include instructions that willcause the processor to display the layout of the data center on adisplay associated with the processor, wherein the layout includes anindication of the cooling performance of the at least one of theplurality of equipment racks. The medium may also include instructionsthat will cause the processor to determine capture index for the atleast one of the plurality of equipment racks based, at least in part,on characteristics of the at least one gap. The sequences of instructionmay further include instructions that will cause the processor todetermine airflow through the at least one gap, including airflow out ofan aisle of the improper cluster through the at least one gap, anddetermine airflow into the aisle through the at least one gap. Thesequences of instruction may also include instructions that will causethe processor to determine a value for capture index for the at leastone of the plurality of equipment racks with each gap in the layoutmodeled as a blank panel, and the sequences of instruction may furtherinclude instructions that will cause the processor to determine acorrector value based on characteristics of the at least one gap, andapply the corrector value to the value for capture index for the atleast one of the plurality of equipment racks.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 shows an example computer system with which various aspects inaccord with the present invention may be implemented;

FIG. 2 illustrates an example distributed system including anembodiment;

FIG. 3 shows a layout of an improper cluster of data equipment racks andcoolers; and

FIG. 4 shows a flowchart of a process for determining coolingcharacteristics in accordance with one embodiment.

DETAILED DESCRIPTION

At least some embodiments in accordance with the present inventionrelate to systems and processes through which a user may design datacenter configurations. These systems may facilitate this design activityby allowing the user to create models of data center configurations fromwhich performance metrics may be determined. Both the systems and theuser may employ these performance metrics to determine alternative datacenter configurations that meet various design objectives. Further, inat least one embodiment, a system will provide an initial layout of datacenter equipment and conduct a cooling analysis on the layout in realtime.

As described in U.S. patent application Ser. No. 12/019,109, titled“System and Method for Evaluating Equipment Rack Cooling”, filed Jan.24, 2008 (referred to herein as “the '109 Application”), and in U.S.patent application Ser. No. 11/342,300, titled “Methods and Systems forManaging Facility Power and Cooling” filed Jan. 27, 2006 (referred toherein as “the '300 application”), both of which are assigned to theassignee of the present application, and both of which are herebyincorporated herein by reference in their entirety, typical equipmentracks in modern data centers draw cooling air in the front of the rackand exhaust air out the rear of the rack. The equipment racks, andin-row coolers are typically arranged in rows in an alternatingfront/back arrangement creating alternating hot and cool aisles in adata center with the front of each row of racks facing the cool aisleand the rear of each row of racks facing the hot aisle. Adjacent rows ofequipment racks separated by a cool aisle may be referred to as a coolaisle cluster, and adjacent rows of equipment racks separated by a hotaisle may be referred to as a hot aisle cluster. As readily apparent toone of ordinary skill in the art, a row of equipment racks may be partof one hot aisle cluster and one cool aisle cluster. In descriptions andclaims herein, equipment in racks, or the racks themselves, may bereferred to as cooling consumers, and in-row cooling units and/orcomputer room air conditioners (CRACs) may be referred to as coolingproviders. In the referenced applications, tools are provided foranalyzing the cooling performance of a cluster of racks in a datacenter. In these tools, multiple analyses may be performed on differentlayouts to attempt to optimize the cooling performance of the datacenter.

In typical prior methods and systems for designing and analyzing thelayout of clusters in a data center, the methods and systems are eitherlimited for use with simple clusters having two equal-length rows and nogaps or openings in the rows, or if not limited to simple clusters,involve the use of complex algorithms that typically cannot be performedin real-time. In data centers, there are many equipment groupings thathave unequal row length or contain gaps and are not proper clusterseasily analyzed by prior techniques. For at least one embodiment, animproper cluster is defined herein as including a two-row grouping ofracks, and potentially coolers, around a common cool or hot aisle inwhich there are gaps in the rows or unequal-length rows. A single rowmay constitute an improper cluster. A continuous break between equipmentin a row greater than three feet may constitute a break in the row andthe row may be divided into multiple proper and improper clusters, orconsidered to be one improper cluster.

In at least one embodiment, a method is provided for predicting thecooling performance of an improper cluster in a data center inreal-time. The method may be incorporated in a system havingcapabilities for predicting the cooling performance of proper clustersand for performing other design and analysis functions of equipment in adata center.

The aspects disclosed herein in accordance with the present invention,are not limited in their application to the details of construction andthe arrangement of components set forth in the following description orillustrated in the drawings. These aspects are capable of assuming otherembodiments and of being practiced or of being carried out in variousways. Examples of specific implementations are provided herein forillustrative purposes only and are not intended to be limiting. Inparticular, acts, elements and features discussed in connection with anyone or more embodiments are not intended to be excluded from a similarrole in any other embodiments.

For example, according to one embodiment of the present invention, acomputer system is configured to perform any of the functions describedherein, including but not limited to, configuring, modeling andpresenting information regarding specific data center configurations.Further, computer systems in embodiments of the data center may be usedto automatically measure environmental parameters in a data center, andcontrol equipment, such as chillers or coolers to optimize performance.Moreover, the systems described herein may be configured to include orexclude any of the functions discussed herein. Thus the invention is notlimited to a specific function or set of functions. Also, thephraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use herein of“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof is meant to encompass the items listed thereafter andequivalents thereof as well as additional items.

Computer System

Various aspects and functions described herein in accordance with thepresent invention may be implemented as hardware or software on one ormore computer systems. There are many examples of computer systemscurrently in use. These examples include, among others, networkappliances, personal computers, workstations, mainframes, networkedclients, servers, media servers, application servers, database serversand web servers. Other examples of computer systems may include mobilecomputing devices, such as cellular phones and personal digitalassistants, and network equipment, such as load balancers, routers andswitches. Further, aspects in accordance with the present invention maybe located on a single computer system or may be distributed among aplurality of computer systems connected to one or more communicationsnetworks.

For example, various aspects and functions may be distributed among oneor more computer systems configured to provide a service to one or moreclient computers, or to perform an overall task as part of a distributedsystem. Additionally, aspects may be performed on a client-server ormulti-tier system that includes components distributed among one or moreserver systems that perform various functions. Thus, the invention isnot limited to executing on any particular system or group of systems.Further, aspects may be implemented in software, hardware or firmware,or any combination thereof. Thus, aspects in accordance with the presentinvention may be implemented within methods, acts, systems, systemelements and components using a variety of hardware and softwareconfigurations, and the invention is not limited to any particulardistributed architecture, network, or communication protocol.

FIG. 1 shows a block diagram of a distributed computer system 100, inwhich various aspects and functions in accord with the present inventionmay be practiced. Distributed computer system 100 may include one morecomputer systems. For example, as illustrated, distributed computersystem 100 includes computer systems 102, 104 and 106. As shown,computer systems 102, 104 and 106 are interconnected by, and mayexchange data through, communication network 108. Network 108 mayinclude any communication network through which computer systems mayexchange data. To exchange data using network 108, computer systems 102,104 and 106 and network 108 may use various methods, protocols andstandards, including, among others, token ring, ethernet, wirelessethernet, Bluetooth, TCP/IP, UDP, Http, FTP, SNMP, SMS, MMS, SS7, Json,Soap, and Corba. To ensure data transfer is secure, computer systems102, 104 and 106 may transmit data via network 108 using a variety ofsecurity measures including TSL, SSL or VPN among other securitytechniques. While distributed computer system 100 illustrates threenetworked computer systems, distributed computer system 100 may includeany number of computer systems and computing devices, networked usingany medium and communication protocol.

Various aspects and functions in accordance with the present inventionmay be implemented as specialized hardware or software executing in oneor more computer systems including computer system 102 shown in FIG. 1.As depicted, computer system 102 includes processor 110, memory 112, bus114, interface 116 and storage 118. Processor 110 may perform a seriesof instructions that result in manipulated data. Processor 110 may be acommercially available processor such as an Intel Pentium, MotorolaPowerPC, SGI MIPS, Sun UltraSPARC, or Hewlett-Packard PA-RISC processor,but may be any type of processor or controller as many other processorsand controllers are available. Processor 110 is connected to othersystem elements, including one or more memory devices 112, by bus 114.

Memory 112 may be used for storing programs and data during operation ofcomputer system 102. Thus, memory 112 may be a relatively highperformance, volatile, random access memory such as a dynamic randomaccess memory (DRAM) or static memory (SRAM). However, memory 112 mayinclude any device for storing data, such as a disk drive or othernon-volatile storage device. Various embodiments in accordance with thepresent invention may organize memory 112 into particularized and, insome cases, unique structures to perform the aspects and functionsdisclosed herein.

Components of computer system 102 may be coupled by an interconnectionelement such as bus 114. Bus 114 may include one or more physicalbusses, for example, busses between components that are integratedwithin a same machine, but may include any communication couplingbetween system elements including specialized or standard computing bustechnologies such as IDE, SCSI, PCI and InfiniBand. Thus, bus 114enables communications, for example, data and instructions, to beexchanged between system components of computer system 102.

Computer system 102 also includes one or more interface devices 116 suchas input devices, output devices and combination input/output devices.Interface devices may receive input or provide output. Moreparticularly, output devices may render information for externalpresentation. Input devices may accept information from externalsources. Examples of interface devices include keyboards, mouse devices,trackballs, microphones, touch screens, printing devices, displayscreens, speakers, network interface cards, etc. Interface devices allowcomputer system 102 to exchange information and communicate withexternal entities, such as users and other systems.

Storage system 118 may include a computer readable and writeablenonvolatile storage medium in which instructions are stored that definea program to be executed by the processor. Storage system 118 also mayinclude information that is recorded, on or in, the medium, and thisinformation may be processed by the program. More specifically, theinformation may be stored in one or more data structures specificallyconfigured to conserve storage space or increase data exchangeperformance. The instructions may be persistently stored as encodedsignals, and the instructions may cause a processor to perform any ofthe functions described herein. The medium may, for example, be opticaldisk, magnetic disk or flash memory, among others. In operation, theprocessor or some other controller may cause data to be read from thenonvolatile recording medium into another memory, such as memory 112,that allows for faster access to the information by the processor thandoes the storage medium included in storage system 118. The memory maybe located in storage system 118 or in memory 112, however, processor110 may manipulate the data within the memory 112, and then copies thedata to the medium associated with storage system 118 after processingis completed. A variety of components may manage data movement betweenthe medium and integrated circuit memory element and the invention isnot limited thereto. Further, the invention is not limited to aparticular memory system or storage system.

Although computer system 102 is shown by way of example as one type ofcomputer system upon which various aspects and functions in accordancewith the present invention may be practiced, aspects of the inventionare not limited to being implemented on the computer system as shown inFIG. 1. Various aspects and functions in accord with the presentinvention may be practiced on one or more computers having a differentarchitectures or components than that shown in FIG. 1. For instance,computer system 102 may include specially-programmed, special-purposehardware, such as for example, an application-specific integratedcircuit (ASIC) tailored to perform a particular operation disclosedherein. While another embodiment may perform the same function usingseveral general-purpose computing devices running MAC OS System X withMotorola PowerPC processors and several specialized computing devicesrunning proprietary hardware and operating systems.

Computer system 102 may be a computer system including an operatingsystem that manages at least a portion of the hardware elements includedin computer system 102. Usually, a processor or controller, such asprocessor 110, executes an operating system which may be, for example, aWindows-based operating system, such as, Windows NT, Windows 2000(Windows ME), Windows XP or Windows Vista operating systems, availablefrom the Microsoft Corporation, a MAC OS System X operating systemavailable from Apple Computer, one of many Linux-based operating systemdistributions, for example, the Enterprise Linux operating systemavailable from Red Hat Inc., a Solaris operating system available fromSun Microsystems, or a UNIX operating system available from varioussources. Many other operating systems may be used, and embodiments arenot limited to any particular implementation.

The processor and operating system together define a computer platformfor which application programs in high-level programming languages maybe written. These component applications may be executable,intermediate, for example, C−, bytecode or interpreted code whichcommunicates over a communication network, for example, the Internet,using a communication protocol, for example, TCP/IP. Similarly, aspectsin accord with the present invention may be implemented using anobject-oriented programming language, such as .Net, SmallTalk, Java,C++, Ada, or C# (C-Sharp). Other object-oriented programming languagesmay also be used. Alternatively, functional, scripting, or logicalprogramming languages may be used.

Additionally, various aspects and functions in accordance with thepresent invention may be implemented in a non-programmed environment,for example, documents created in HTML, XML or other format that, whenviewed in a window of a browser program, render aspects of agraphical-user interface or perform other functions. Further, variousembodiments in accord with the present invention may be implemented asprogrammed or non-programmed elements, or any combination thereof. Forexample, a web page may be implemented using HTML while a data objectcalled from within the web page may be written in C++. Thus, theinvention is not limited to a specific programming language and anysuitable programming language could also be used. Further, in at leastone embodiment, the tool may be implemented using VBA Excel.

A computer system included within an embodiment may perform additionalfunctions outside the scope of the invention. For instance, aspects ofthe system may be implemented using an existing commercial product, suchas, for example, Database Management Systems such as SQL Serveravailable from Microsoft of Seattle Wash., Oracle Database from Oracleof Redwood Shores, Calif., and MySQL from MySQL AB of Uppsala, Sweden orintegration software such as Web Sphere middleware from IBM of Armonk,N.Y. However, a computer system running, for example, SQL Server may beable to support both aspects in accord with the present invention anddatabases for sundry applications not within the scope of the invention.

Example System Architecture

FIG. 2 presents a context diagram including physical and logicalelements of distributed system 200. As shown, distributed system 200 isspecially configured in accordance with the present invention. Thesystem structure and content recited with regard to FIG. 2 is forexemplary purposes only and is not intended to limit the invention tothe specific structure shown in FIG. 2. As will be apparent to one ofordinary skill in the art, many variant system structures can bearchitected without deviating from the scope of the present invention.The particular arrangement presented in FIG. 2 was chosen to promoteclarity.

Information may flow between the elements, components and subsystemsdepicted in FIG. 2 using any technique. Such techniques include, forexample, passing the information over the network via TCP/IP, passingthe information between modules in memory and passing the information bywriting to a file, database, or some other non-volatile storage device.Other techniques and protocols may be used without departing from thescope of the invention.

Referring to FIG. 2, system 200 includes user 202, interface 204, datacenter design and management system 206, communications network 208 anddata center database 210. System 200 may allow user 202, such as a datacenter architect or other data center personnel, to interact withinterface 204 to create or modify a model of one or more data centerconfigurations. According to one embodiment, interface 204 may includeaspects of the floor editor and the rack editor as disclosed in PatentCooperation Treaty Application No. PCT/US08/63675, entitled METHODS ANDSYSTEMS FOR MANAGING FACILITY POWER AND COOLING, filed on May 15, 2008,which is incorporated herein by reference in its entirety and ishereinafter referred to as PCT/US08/63675. In other embodiments,interface 204 may be implemented with specialized facilities that enableuser 202 to design, in a drag and drop fashion, a model that includes arepresentation of the physical layout of a data center or any subsetthereof. This layout may include representations of data centerstructural components as well as data center equipment. The features ofinterface 204, as may be found in various embodiments in accordance withthe present invention, are discussed further below. In at least oneembodiment, information regarding a data center is entered into system200 through the interface, and assessments and recommendations for thedata center are provided to the user. Further, in at least oneembodiment, optimization processes may be performed to optimize coolingperformance and energy usage of the data center.

As shown in FIG. 2, data center design and management system 206presents data design interface 204 to user 202. According to oneembodiment, data center design and management system 206 may include thedata center design and management system as disclosed in PCT/US08/63675.In this embodiment, design interface 204 may incorporate functionalityof the input module, the display module and the builder module includedin PCT/US08/63675 and may use the database module to store and retrievedata.

As illustrated, data center design and management system 206 mayexchange information with data center database 210 via network 208. Thisinformation may include any information required to support the featuresand functions of data center design and management system 206. Forexample, in one embodiment, data center database 210 may include atleast some portion of the data stored in the data center equipmentdatabase described in PCT/US08/63675. In another embodiment, thisinformation may include any information required to support interface204, such as, among other data, the physical layout of one or more datacenter model configurations, the production and distributioncharacteristics of the cooling providers included in the modelconfigurations, the consumption characteristics of the cooling consumersin the model configurations, and a listing of equipment racks andcooling providers to be included in a cluster.

In one embodiment, data center database 210 may store types of coolingproviders, the amount of cool air provided by each type of coolingprovider, and a temperature of cool air provided by the coolingprovider. Thus, for example, data center database 210 includes recordsof a particular type of CRAC unit that is rated to deliver airflow atthe rate of 5,600 cfm at a temperature of 68 degrees Fahrenheit. Inaddition, the data center database 210 may store one or more coolingmetrics, such as inlet and outlet temperatures of the CRACs and inletand outlet temperatures of one or more equipment racks. The temperaturesmay be periodically measured and input into the system, or in otherembodiments, the temperatures may be continuously monitored usingdevices coupled to the system 200.

Data center database 210 may take the form of any logical constructioncapable of storing information on a computer readable medium including,among other structures, flat files, indexed files, hierarchicaldatabases, relational databases or object oriented databases. The datamay be modeled using unique and foreign key relationships and indexes.The unique and foreign key relationships and indexes may be establishedbetween the various fields and tables to ensure both data integrity anddata interchange performance.

The computer systems shown in FIG. 2, which include data center designand management system 206, network 208 and data center equipmentdatabase 210, each may include one or more computer systems. Asdiscussed above with regard to FIG. 1, computer systems may have one ormore processors or controllers, memory and interface devices. Theparticular configuration of system 200 depicted in FIG. 2 is used forillustration purposes only and embodiments of the invention may bepracticed in other contexts. Thus, embodiments of the invention are notlimited to a specific number of users or systems.

In at least one embodiment, which will now be described, a tool isprovided that predicts cooling performance for an improper hot aislecluster in real time and displays results of the prediction along with amodel of the improper cluster. FIG. 3 shows a model of an impropercluster of racks 300. The improper cluster includes two rows of racks Aand B separated by a hot aisle 302. Each row includes racks R andcoolers C. Row A includes 6 racks and 3 coolers and Row B includes 4racks and 1 cooler. Row B also includes two gaps 304 and 306. In oneembodiment, the racks are standard nineteen inch equipment racks havingan overall width of 24 inches and the coolers have a width of 12 inches.However, embodiments of the invention may be used with racks and coolersof other sizes. Gap 304 is approximately two feet wide and gap 306,representing a difference in lengths of the Rows A and B, isapproximately 4 feet wide. As shown in FIG. 3, in accordance with someembodiments, the results of the prediction of cooling performance may bedisplayed directly on the racks in the model. In FIG. 3, the results areshown as capture index (in percentage) on each of the racks in themodel.

In embodiments of the invention, different performance metrics can beused to evaluate the cooling performance of an improper cluster. In oneembodiment, the performance metric is capture index. Capture index, andmethods of determining capture index are described in greater detail inthe '109 and '300 applications referenced above. The cold-aisle captureindex for a rack is defined in at least some embodiments as the fractionof air ingested by the rack which originates from local coolingresources (e.g., perforated floor tiles or local coolers). The hot-aislecapture index is defined as the fraction of air exhausted by a rackwhich is captured by local extracts (e.g., local coolers or returnvents). CI therefore varies between 0 and 100% with better coolingperformance generally indicated by greater CI values. In a cold-aisleanalysis, high CI's ensure that the bulk of the air ingested by a rackcomes from local cooling resources rather than being drawn from the roomenvironment or from air which may have already been heated byelectronics equipment. In this case, rack inlet temperatures willclosely track the perforated-tile airflow temperatures and, assumingthese temperatures are within the desired range, acceptable cooling willbe achieved. In a hot-aisle analysis, high CI's ensure that rack exhaustis captured locally and there is little heating of the surrounding roomenvironment.

While good (high) CI values typically imply good cooling performance;low CI values do not necessarily imply unacceptable cooling performance.For example, in a rack in a raised-floor environment which draws most ofits airflow from the surrounding room environment rather than from theperforated tiles, the rack's cold-aisle CI will be low; however, if thesurrounding room environment is sufficiently cool, the rack's inlettemperature may still be acceptable. In this case, the rack's coolingneeds are met by the external room environment rather than perforatedtiles within the rack's cluster. If this process is repeated many timesacross the data center, facility cooling will be complex and may beunpredictable. High CI values lead to inherently scalable clusterlayouts and more predictable room environments.

In one embodiment, a tool operable on one or more computer systemsdescribed above determines capture index for racks of an impropercluster. In doing so, the improper cluster is first analyzed as a propercluster, with all gaps and any row-length mismatches filled withblanking panels (or “dummy” racks with zero airflow). The capture indexis determined for each of the racks in the proper cluster using any of anumber of techniques for determining the CI, including the algebraic,neural network and PDA-CFD techniques described in the '109 and '300applications referenced above.

Once the CI is determined for the proper cluster, a corrector model isapplied to the results to correct for the negative effects created bygaps in the improper cluster. Gaps in the rows of an improper clusterprovide an opening to allow air to escape from the aisle between the tworows in a cluster having a negative effect on the capture index. Thecorrector model determines the percentage of reduction of the CI foreach rack in the improper cluster. The final CI for a rack i in animproper cluster can be expressed as follows using equation (1):CI _(i) ^(gap) =CI _(i) ^(blank)·(1−Corrector_(i))  Equation (1)where,

CI_(i) ^(blank) is the “benchmark” CI value for rack i when all the gapsand row length mismatches in the cluster are replaced by blankingpanels.

Corrector_(i) is the percentage reduction (expressed as a decimal value)of the CI of rack i.

In equations discussed herein, rack locations are designated as A_(i)and B_(j). The designation A or B indicates which row the rack iscontained in and the subscript i or j indicates the slot in the rowcontaining the rack, which may be counted from left or right. Forexample, in the cluster of FIG. 3, Row A includes 6 racks and 3 coolersfor a total of 9 objects or 30 6-inch slots and Row B includes 4 racks,one cooler and two gaps for a totally of 7 objects or 30 6-inch slots.

The CI corrector is related to the distance between the rack of interestand all the gaps in the improper cluster where each 6-inch “slot” (in anopen “gap section” like 304 or 306 of FIG. 3) is typically consideredone gap. In one embodiment, the percentage reduction of the CI value ofa certain rack at location A_(i) can be adequately represented asfollows:

$\begin{matrix}{{Corrector}_{Ai} = {X \cdot \frac{\begin{matrix}{{\sum\limits_{j}^{{all}\mspace{14mu}{gaps}\mspace{14mu} i\; n\mspace{14mu}{row}\mspace{14mu} A}\;{\mathbb{e}}^{{{- Y} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}} +} \\{Z \cdot {\sum\limits_{j}^{{all}\mspace{14mu}{gaps}\mspace{14mu} i\; n\mspace{14mu}{row}\mspace{14mu} B}\;{\mathbb{e}}^{{{- Y} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}}\end{matrix}}{\begin{matrix}{{\sum\limits_{j}^{{all}\mspace{14mu}{objects}\mspace{14mu} i\; n\mspace{14mu}{row}\mspace{14mu} A}\;{\mathbb{e}}^{{{- Y} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}} +} \\{Z \cdot {\sum\limits_{j}^{{all}\mspace{14mu}{objects}\mspace{14mu} i\; n\mspace{14mu}{row}\mspace{14mu} B}\;{\mathbb{e}}^{{{- Y} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}}\end{matrix}}}} & {{Equation}\mspace{20mu}(2)}\end{matrix}$

where,

Corrector_(Ai) is the percentage reduction (expressed as a decimalvalue) of the CI of rack i in Row A

X, Y, and Z are empirical constants

Δx(i,j) is the horizontal distance between locations (slots) i and j.

In equation (2), the numerator is equal to zero when there are no gapsand hence the Corrector_(Ai) becomes zero. In this case, the cluster isa proper cluster and no correction is needed. The closer a gap is to arack, the larger the Corrector for that rack and hence the lower the CIof the rack. The empirical constants X, Y and Z can be determined bycomparing the calculations for a large number of layouts tocorresponding “benchmark” cases typically created by CFD simulation. Theconstants in the model are then adjusted to give the best overallagreement with the benchmark cases based on various metrics includingCI.

The Corrector described herein can be applied to any type of impropercluster, which might include hot aisle improper clusters with row-basedcoolers and cold aisle improper clusters with row-based coolers and/orperforated tiles.

In one example, for hot aisle improper clusters with row-based coolers,constants X and Y are fixed for all cluster configurations, but constantZ varies with aisle width as shown in the table below. For a single-rowimproper cluster, Z is 2.

TABLE 1 Constants vs. Aisle Width for Hot Aisle Clusters with Row-BasedCoolers Hot Aisle Width (ft) Constant 3 4 5 6 X 1 1 1 1 Y 0.073 0.0730.073 0.073 Z 3 2.33 1.67 1

In one example, for cold aisle improper clusters with row-based coolers,constants X and Y are fixed for all cluster configurations, but theconstant Z varies with aisle width as shown in the table below. For asingle row improper cluster, Z is 2.

TABLE 2 Constants vs. Aisle Width for Cold Aisle Clusters with Row-BasedCoolers Cold Aisle Width (ft) Constant 4 6 X 1 1 Y 0.123 0.123 Z 3 1

In one example, for cold aisle improper clusters with perforated tiles,constants X and Y are fixed for all cluster configurations, but constantZ varies with aisle width as shown in the table below. For a single rowimproper cluster, Z is 0.18.

TABLE 3 Constants vs. Aisle Width for Cold Aisle Clusters withPerforated Tiles Cold Aisle Width (ft) Constant 4 6 X 1 1 Y 2.8 2.8 Z0.1 0.05

In one example, for cold aisle improper clusters with both row-basedcoolers and perforated tiles, the calculation of constant X can beexpressed as follows using equation (3):X=α·X _(PT)+(1−α)·X _(IR)  Equation (3)

where, X_(PT) is the constant X used in thecold-aisle-cluster-with-perforated-tiles applications for the same coldaisle width; X_(IR) is the constant X used in the cold aislecluster-with-row-based-coolers applications for the same cold aislewidth; and α is the fraction of supply airflow delivered by theperforated tiles. Constants Y and Z are be determined in a similarmanner.

It is noted that the effect of the presence of gaps on coolingperformance is greater for layouts with row-based coolers than forraised floor applications due to the strong horizontal airflow patternsassociated with row-based coolers.

FIG. 4 provides a flow chart of a process 400 for determining the CI forequipment racks of an improper cluster using the corrector modeldescribed above in accordance with one embodiment. First at stage 402 ofthe process 400, information regarding the layout for the impropercluster is loaded into the tool. The information may include powerconsumption values for each of the racks, cooling capacities of each ofthe coolers and location of the racks and coolers in the layout. Atleast some of the information may have previously been stored in adatabase contained in a computer system implementing the tooltransmitted to the computer system via a network or loaded into thesystem by a user. At stage 404, gaps are identified in the impropercluster, and the gaps (including mismatches at the end of a row) arecovered in the layout with blanking panels or replaced with racks havingno airflow. The CI for each rack in the improper cluster is thendetermined (stage 406) using one of a number of techniques as discussedabove. As part of stage 406, the Corrector for each rack is determinedand applied to the CI. At stage 408, the model is then displayed (andmay be printed) with the corrected CI for each of the racks. At stage410, the process ends. In some embodiments, the displayed model mayprovide additional indications for out of tolerance CI values, such aswarning labels or through the use of color codes. Further, in someembodiments, when an out of tolerance condition is indicated, a user maybe prompted to rearrange the racks to find a more satisfactory solution.

In another embodiment, a tool for determining CI for equipment racks inimproper hot aisle clusters utilizes an embedded algebraic model. Thisembedded algebraic model may be included within existing algebraicmodels (or similar models) for proper clusters or may be implemented asa stand alone tool. One example of an algebraic model with which theembedded algebraic model may be used is described in the '109 and '300applications referenced above.

In the embedded algebraic model of one embodiment, airflow which passesthrough gaps is explicitly estimated in order to account for theunfavorable effects of gaps on the CI of each rack. In general, thereare two types of airflows through any given gap: an inflow, Q_(gap in),and an outflow, Q_(gap out). Both “in” and “out” airflows may besimultaneously present in the same gap. The model of airflows used in atleast one embodiment of the tool need not have a direct, accuratephysical interpretation for the tool to be effective. In other words, ifthe “in” term computed by the tool is, for example, 375 cfm; it does notmean that the actual inflow through the gap must be 375 cfm for the toolto be effective. Rather, the use of the “in” and “out” flow terms allowfor more degrees of freedom in the tool with which the tool may be“tuned.” The inflow, Q_(gap in), is determined by the cooler airflowrate as well as the distance between all the coolers and the gap. It hasbeen found that the airflow rate which comes in through a gap i in row Ainto a hot aisle can be adequately represented as shown in Equation (4)below:

$\begin{matrix}{\left( Q_{Ai} \right)_{{gap}\mspace{14mu} i\; n} = {{\sum\limits_{{{all}\mspace{14mu} j} \neq i}^{{all}\mspace{14mu}{coolers}\mspace{14mu} j\mspace{14mu} i\; n\mspace{14mu}{Row}\mspace{14mu} A}\;{\left( Q_{Aj} \right)_{{cap}\mspace{14mu}{self}} \cdot X_{1} \cdot {\mathbb{e}}^{{{- Y_{1}} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}} + {X_{3} \cdot {\sum\limits_{{{all}\mspace{14mu} j} \neq i}^{{all}\mspace{14mu}{coolers}\mspace{14mu} j\mspace{14mu} i\; n\mspace{14mu}{Row}\mspace{14mu} B}\;{\left( Q_{Bj} \right)_{{cap}\mspace{14mu}{self}} \cdot X_{1} \cdot {\mathbb{e}}^{{{- Y_{1}} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}}}}} & {{Equation}\mspace{20mu}(4)}\end{matrix}$

where,

(Q_(Ai))_(gap in) is the airflow rate that comes in through the gap atlocation A_(i)

(Q_(Aj))_(cap self) is the airflow rate captured by the cooler atlocation A_(j)

(Q_(Bj))_(cap self) is the airflow rate captured by the cooler atlocation B_(j)

Δx(i,j) is the horizontal distance between locations (slots) i and j

X₁ and Y₁ are empirical constants and X₃ is the empirical “coupling”constant accounting for effects from the opposite row.

Similarly, the outflow, Q_(gap out) can be determined by the rackairflow rate as well as the distance between all the racks and the gap.It has been found that the airflow rate which leaves the hot aislethrough a gap i in row A can be adequately represented as:

$\begin{matrix}{\left( Q_{Ai} \right)_{{gap}\mspace{14mu}{out}} = {{\sum\limits_{{{all}\mspace{14mu} j} \neq i}^{{all}\mspace{14mu}{rack}\mspace{14mu} j\mspace{14mu} i\; n\mspace{14mu}{Row}\mspace{14mu} A}\;{\left( Q_{Aj} \right)_{\sup\mspace{14mu}{self}} \cdot X_{2} \cdot {\mathbb{e}}^{{{- Y_{2}} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}} + {Y_{3} \cdot {\sum\limits_{{{all}\mspace{14mu} j} \neq i}^{{all}\mspace{14mu}{rack}\mspace{14mu} j\mspace{14mu} i\; n\mspace{14mu}{Row}\mspace{14mu} B}\;{\left( Q_{Bj} \right)_{\sup\mspace{14mu}{self}} \cdot X_{2} \cdot {\mathbb{e}}^{{{- Y_{2}} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}}}}} & {{Equation}\mspace{20mu}(5)}\end{matrix}$

where,

(Q_(Ai))_(gap out) is the airflow rate that leaves through the gap atlocation A_(i)

(Q_(Aj))_(sup self) is the airflow rate supplied by the rack at locationA_(j)

(Q_(Bj))_(sup self) is the airflow rate supplied by the rack at locationB_(j)

Δx(i,j) is the horizontal distance between locations (slots) i and j

X₂ and Y₂ are empirical constants and Y₃ is the empirical “coupling”constant accounting for effects from the opposite row. The constantsare, again, determined by comparing the calculations for a large numberof layouts to corresponding “benchmark” cases typically created by CFDsimulation. The constants in the model are then adjusted to give thebest overall agreement with the benchmark cases based on various metricsincluding CI.

When calculating the net airflow that can be supplied to a particularlocation A_(i) using the current hot-aisle algebraic calculator, thetool accounts for the airflow which escapes the hot aisle through allthe gaps. To accomplish this, two extra terms are subtracted from theequation to calculate the (Q_(Aj))_(cap net) (from the '109 Applicationreferenced above) one for row A and one for Row B as shown by Equation(6) below:

$\begin{matrix}{\left( Q_{Ai} \right)_{{cap}\mspace{14mu}{net}} = {\left( Q_{Ai} \right)_{{cap}\mspace{14mu}{self}} + {\sum\limits_{\substack{{{all}\mspace{14mu} j} \neq i \\ {and}\mspace{14mu} j\mspace{14mu}{is}\mspace{14mu}{not}\mspace{14mu} a\mspace{14mu}{gap}}}\;{\left( Q_{Aj} \right)_{{cap}\mspace{14mu}{self}} \cdot A \cdot {\mathbb{e}}^{{- B}\;\Delta\;{x{({{\mathbb{i}},j})}}}}} - {\sum\limits_{\substack{{{all}\mspace{14mu} j} \neq i \\ {and}\mspace{14mu} j\mspace{14mu}{is}\mspace{14mu} a\mspace{14mu}{gap}}}\;{\left( Q_{Aj} \right)_{{gap}\mspace{14mu}{out}} \cdot X_{4} \cdot {\mathbb{e}}^{{{- Y_{4}} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}} + {C \cdot \left\{ {\left( Q_{Bi} \right)_{{cap}\mspace{14mu}{self}} + {\sum\limits_{\substack{{{all}\mspace{14mu} j} \neq i \\ {and}\mspace{14mu} j\mspace{14mu}{is}\mspace{14mu}{not}\mspace{14mu} a\mspace{14mu}{gap}}}\;{\left( Q_{Bj} \right)_{{cap}\mspace{14mu}{self}} \cdot A \cdot {\mathbb{e}}^{{- B}\;\Delta\;{x{({{\mathbb{i}},j})}}}}} - {\sum\limits_{\substack{{{all}\mspace{14mu} j} \neq i \\ {and}\mspace{14mu} j\mspace{14mu}{is}\mspace{14mu} a\mspace{14mu}{gap}}}\;{\left( Q_{Bj} \right)_{{gap}\mspace{14mu}{out}} \cdot X_{4} \cdot {\mathbb{e}}^{{{- Y_{4}} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}}} \right\}}}} & {{Equation}\mspace{20mu}(6)}\end{matrix}$

where,

(Q_(Ai))_(cap net) is the net maximum airflow that can be captured atlocation A_(i) including contributions from all coolers in the cluster

(Q_(Aj))_(cap self) is the airflow captured by the cooler at locationA_(j)

(Q_(Bj))_(cap self) is the airflow captured by the cooler at locationB_(j)

(Q_(Aj))_(gap out) is the airflow rate that leaves through the gap atlocation A_(j)

(Q_(Bj))_(gap out) is the airflow rate that leaves through the gap atlocation B_(j)

Δx(i,j) is the horizontal distance between locations (slots) i and j

A, B, X₄, and Y₄ are empirical constants

C is an empirical “coupling” constant accounting for effects from theopposite row

Similarly, the airflow which enters the hot aisle through gaps can alsobe accounted for by adding two extra terms, Q_(gap in) one for row A andone for row B, when calculating the (Q_(Aj))_(sup net) as shown byEquation (7) below:

$\begin{matrix}{\left( Q_{Ai} \right)_{\sup\mspace{14mu}{net}} = {\left( Q_{Ai} \right)_{\sup\mspace{14mu}{self}} + {\sum\limits_{\substack{{{all}\mspace{14mu} j} \neq i \\ {and}\mspace{14mu} j\mspace{14mu}{is}\mspace{14mu}{not}\mspace{14mu} a\mspace{14mu}{gap}}}\;{\left( Q_{Aj} \right)_{\sup\mspace{14mu}{self}} \cdot E \cdot {\mathbb{e}}^{{- F}\;\Delta\;{x{({{\mathbb{i}},j})}}}}} + {\sum\limits_{\substack{{{all}\mspace{14mu} j} \neq i \\ {and}\mspace{14mu} j\mspace{14mu}{is}\mspace{14mu} a\mspace{14mu}{gap}}}\;{\left( Q_{Aj} \right)_{{gap}\mspace{14mu} i\; n} \cdot X_{5} \cdot {\mathbb{e}}^{{{- Y_{5}} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}} + {D \cdot \left\{ {\left( Q_{Bi} \right)_{\sup\mspace{14mu}{self}} + {\sum\limits_{\substack{{{all}\mspace{14mu} j} \neq i \\ {and}\mspace{14mu} j\mspace{14mu}{is}\mspace{14mu}{not}\mspace{14mu} a\mspace{14mu}{gap}}}\;{\left( Q_{Bj} \right)_{\sup\mspace{14mu}{self}} \cdot E \cdot {\mathbb{e}}^{{- F}\;\Delta\;{x{({{\mathbb{i}},j})}}}}} + {\sum\limits_{\substack{{{all}\mspace{14mu} j} \neq i \\ {and}\mspace{14mu} j\mspace{14mu}{is}\mspace{14mu} a\mspace{14mu}{gap}}}\;{\left( Q_{Bj} \right)_{{gap}\mspace{14mu} i\; n} \cdot X_{5} \cdot {\mathbb{e}}^{{{- Y_{5}} \cdot \Delta}\;{x{({{\mathbb{i}},j})}}}}}} \right\}}}} & {{Equation}\mspace{20mu}(7)}\end{matrix}$

where,

(Q_(Ai))_(sup net) is the net maximum airflow that can be supplied tolocation A_(i) including contributions from all racks in the cluster

(Q_(Aj))_(sup self) is the airflow supplied by the rack at locationA_(j)

(Q_(Bj))_(sup self) is the airflow supplied by the rack at locationB_(j)

(Q_(Aj))_(gap in) is the airflow rate that comes in through the gap atlocation A_(j)

(Q_(Bj))_(gap in) is the airflow rate that comes in through the gap atlocation B_(j)

Δx(i,j) is the horizontal distance between locations (slots) i and j

E, F, X₅, and Y₅ are empirical constants

D is an empirical “coupling” constant accounting for effects from theopposite row

All the empirical constants can be determined by comparing thecalculations for a large number of layouts to corresponding “benchmark”cases typically created by CFD simulation. The constants in the modelare then adjusted to give the best overall agreement with the benchmarkcases based on various metrics including CI.

In one example, empirical constants are determined as shown in thefollowing table:

TABLE 4 Constants of Imbedded Model for Hot Aisle Clusters X1 1.000 Y112.736 X2 1.000 Y2 2.486 X3 10.517 Y3 0.018 X4 1.000 Y4 0.019 X5 1.000Y5 8.548

The CI is then equal to the ratio of net airflow captured and netairflow supplied at any location expressed as a percentage with valuescapped at 100%.

In the embodiments above, an embedded algebraic model is provided fordetermining capture index for equipment racks in the hot aisle of animproper cluster of racks. As readily understood by one of ordinaryskill in the art, analogous models may be created for determining thecapture index for equipment racks in other layouts including cold aisleswith row-based coolers, cold aisles with perforated tiles, and coldaisles with both row-based coolers and perforated tiles.

Using the algebraic method described above, the CI for racks in animproper cluster may be determined. The results of the analysis can thenbe used to layout equipment in a data center as described above or torearrange the layout to ensure that specified cooling requirements aremet.

In one embodiment, calculations are typically performed for every 6-inchslot along both rows of a cluster so that the tool may be used withstandard-width equipment racks; results are averaged over the actualentire rack width before being presented.

In methods of at least one embodiment of the invention, after successfulmodeling of an improper cluster, the results of the model may be used aspart of a system to order equipment, ship equipment and installequipment in a data center.

In at least some embodiments of the invention discussed herein, theperformance of assessments and calculations in real-time refers toprocesses that are completed in a matter of a few seconds or less ratherthan several minutes or longer as can happen with complex calculations,such as those involving typical CFD calculations.

Having thus described several aspects of at least one embodiment of thisinvention, it is to be appreciated various alterations, modifications,and improvements will readily occur to those skilled in the art. Suchalterations, modifications, and improvements are intended to be part ofthis disclosure, and are intended to be within the spirit and scope ofthe invention. Accordingly, the foregoing description and drawings areby way of example only.

1. A computer-implemented method for evaluating equipment in an impropercluster in a data center, the equipment including a plurality ofequipment racks, and at least one cooling provider, the methodcomprising: receiving, by a computer system, data regarding each of theplurality of equipment racks and the at least one cooling provider, thedata including a layout of the improper cluster of equipment racks andthe at least one cooling provider; storing the received data;identifying at least one gap in the layout; determining a performancemetric for the at least one of the plurality of equipment racks;determining a corrector value based on characteristics of the at leastone gap; applying the corrector value to the performance metric for theat least one of the plurality of equipment racks; determining, by thecomputer system, cooling performance of at least one of the plurality ofequipment racks based, at least in part, on the corrector value and thecharacteristics of the at least one gap; and displaying the layout ofthe data center, wherein the layout includes an indication of thecooling performance of the at least one of the plurality of equipmentracks.
 2. The computer-implemented method of claim 1, whereindetermining the performance metric includes determining a capture indexfor the at least one of the plurality of equipment racks.
 3. Thecomputer-implemented method of claim 2, further comprising determiningthe capture index for each of the plurality of equipment racks based, atleast in part, on characteristics of the at least one gap.
 4. Thecomputer-implemented method of claim 3, wherein determining captureindex includes determining airflow through the at least one gap.
 5. Thecomputer-implemented method of claim 4, wherein determining airflowincludes determining airflow out of an aisle of the improper clusterthrough the at least one gap, and determining airflow into the aislethrough the at least one gap.
 6. The computer-implemented method ofclaim 1, wherein determining the performance metric includes determininga value for capture index for the at least one of the plurality ofequipment racks with each gap in the layout modeled as a blank panel. 7.The computer-implemented method of claim 6, further comprising applyingthe corrector value to the value for capture index for the at least oneof the plurality of equipment racks.
 8. The computer-implemented methodof claim 7, further comprising positioning the equipment in the datacenter in accordance with the layout.
 9. The computer-implemented methodof claim 4, further comprising positioning the equipment in the datacenter in accordance with the layout.
 10. A system for evaluatingequipment in an improper cluster in a data center, the equipmentincluding a plurality of equipment racks, and at least one coolingprovider, the system comprising: an interface; and a controllerconfigured to: receive data regarding each of the plurality of equipmentracks and the at least one cooling provider, the data including a layoutof the improper cluster of equipment racks and the at least one coolingprovider; store the received data in the system; identify at least onegap in the layout; determine a performance metric for the at least oneof the plurality of equipment racks; determine a corrector value basedon characteristics of the at least one gap; apply the corrector value tothe performance metric for the at least one of the plurality ofequipment racks; and determine cooling performance of at least one ofthe plurality of equipment racks based, at least in part, on thecorrector value and the characteristics of the at least one gap.
 11. Thesystem of claim 10, further comprising a display coupled to thecontroller, and wherein the controller is further configured to displaythe layout of the data center, wherein the layout includes an indicationof the cooling performance of the at least one of the plurality ofequipment racks.
 12. The system of claim 11, wherein the controller isconfigured to determine the performance metric for the at least one ofthe plurality of equipment racks by determining a capture index for theat least one of the plurality of equipment racks.
 13. The system ofclaim 12, wherein the controller is further configured to determine thecapture index for each of the plurality of equipment racks based, atleast in part, on characteristics of the at least one gap.
 14. Thesystem of claim 13, wherein the controller is configured to determineairflow through the at least one gap.
 15. The system of claim 14,wherein the controller is further configured to determine airflow out ofan aisle of the improper cluster through the at least one gap, anddetermine airflow into the aisle through the at least one gap.
 16. Thesystem of claim 10, wherein the controller is configured to determinethe performance metric by determining a value for capture index for theat least one of the plurality of equipment racks with each gap in thelayout modeled as a blank panel.
 17. The system of claim 16, wherein thecontroller is further configured to apply the corrector value to thevalue for capture index for the at least one of the plurality ofequipment racks.
 18. A non-transitory computer readable medium havingstored thereon sequences of instructions including instructions thatwill cause a processor to: receive data regarding a layout of animproper cluster of equipment racks and at least one cooling provider;store the received data; identify at least one gap in the layout;determine a performance metric for the at least one of the plurality ofequipment racks; determine a corrector value based on characteristics ofthe at least one gap; apply the corrector value to the performancemetric for the at least one of the plurality of equipment racks; anddetermine cooling performance of at least one of the plurality ofequipment racks based, at least in part, on the corrector value and thecharacteristics of the at least one gap.
 19. The non-transitory computerreadable medium of claim 18, wherein the sequences of instructionfurther include instructions that will cause the processor to: displaythe layout of the data center on a display associated with theprocessor, wherein the layout includes an indication of the coolingperformance of the at least one of the plurality of equipment racks. 20.The non-transitory computer readable medium of claim 19, wherein thesequences of instruction further include instructions that will causethe processor to determine the performance metric by determining acapture index for the at least one of the plurality of equipment racks.